| 1. |
- Dudas, Catarina, et al.
(författare)
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Integration of data mining and multi-objective optimisation for decision support in production system development
- 2014
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Ingår i: International journal of computer integrated manufacturing (Print). - : Taylor & Francis. - 0951-192X .- 1362-3052. ; 27:9, s. 824-839
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Tidskriftsartikel (refereegranskat)abstract
- Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.
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| 2. |
- Ng, Amos H. C., 1970-, et al.
(författare)
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Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster Convergence
- 2013. - 1
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Ingår i: Learning and Intelligent Optimization. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642449727 - 9783642449734 ; , s. 1-18
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Bokkapitel (refereegranskat)abstract
- This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.
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